Artificial Intelligence for Business: A Roadmap for Getting Started with AI will provide the reader with an easy to understand roadmap for how to take an organization through the adoption of AI technology. It will first help with the identification of which business problems and opportunities are right for AI and how to prioritize them to maximize the likelihood of success. Specific methodologies are introduced to help with finding critical training data within an organization and how to fill data gaps if they exist. With data in hand, a scoped prototype can be built to limit risk and provide tangible value to the organization as a whole to justify further investment. Finally, a production level AI system can be developed with best practices to ensure quality with not only the application code, but also the AI models. Finally, with this particular AI adoption journey at an end, the authors will show that there is additional value to be gained by iterating on this AI adoption lifecycle and improving other parts of the organization.
By:
Jason L. Anderson,
Jeffrey L. Coveyduc
Imprint: John Wiley & Sons Inc
Country of Publication: United States
Dimensions:
Height: 231mm,
Width: 158mm,
Spine: 33mm
Weight: 431g
ISBN: 9781119651734
ISBN 10: 1119651735
Pages: 240
Publication Date: 25 March 2020
Audience:
General/trade
,
ELT Advanced
Format: Hardback
Publisher's Status: Active
Preface ix Acknowledgments xi Chapter 1 Introduction 1 Case Study #1: FANUC Corporation 2 Case Study #2: H&R Block 4 Case Study #3: BlackRock, Inc. 5 How to Get Started 6 The Road Ahead 10 Notes 11 Chapter 2 Ideation 13 An Artificial Intelligence Primer 13 Becoming an Innovation-Focused Organization 23 Idea Bank 25 Business Process Mapping 27 Flowcharts, SOPs, and You 28 Information Flows 29 Coming Up with Ideas 31 Value Analysis 31 Sorting and Filtering 34 Ranking, Categorizing, and Classifying 35 Reviewing the Idea Bank 37 Brainstorming and Chance Encounters 38 AI Limitations 41 Pitfalls 44 Action Checklist 45 Notes 46 Chapter 3 Defining the Project 47 The What, Why, and How of a Project Plan 48 The Components of a Project Plan 49 Approaches to Break Down a Project 53 Project Measurability 62 Balanced Scorecard 63 Building an AI Project Plan 64 Pitfalls 66 Action Checklist 69 Chapter 4 Data Curation and Governance 71 Data Collection 73 Leveraging the Power of Existing Systems 81 The Role of a Data Scientist 81 Feedback Loops 82 Making Data Accessible 84 Data Governance 85 Are You Data Ready? 89 Pitfalls 90 Action Checklist 94 Notes 94 Chapter 5 Prototyping 97 Is There an Existing Solution? 97 Employing vs. Contracting Talent 99 Scrum Overview 101 User Story Prioritization 103 The Development Feedback Loop 105 Designing the Prototype 106 Technology Selection 107 Cloud APIs and Microservices 110 Internal APIs 112 Pitfalls 112 Action Checklist 114 Notes 114 Chapter 6 Production 117 Reusing the Prototype vs. Starting from a Clean Slate 117 Continuous Integration 119 Automated Testing 124 Ensuring a Robust AI System 128 Human Intervention in AI Systems 129 Ensure Prototype Technology Scales 131 Cloud Deployment Paradigms 133 Cloud API’s SLA 135 Continuing the Feedback Loop 135 Pitfalls 135 Action Checklist 137 Notes 137 Chapter 7 Thriving with an AI Lifecycle 139 Incorporate User Feedback 140 AI Systems Learn 142 New Technology 144 Quantifying Model Performance 145 Updating and Reviewing the Idea Bank 147 Knowledge Base 148 Building a Model Library 150 Contributing to Open Source 155 Data Improvements 157 With Great Power Comes Responsibility 158 Pitfalls 159 Action Checklist 161 Notes 161 Chapter 8 Conclusion 163 The Intelligent Business Model 164 The Recap 164 So What are You Waiting For? 168 Appendix A AI Experts 169 AI Experts 169 Chris Ackerson 169 Jeff Bradford 173 Nathan S. Robinson 175 Evelyn Duesterwald 177 Jill Nephew 179 Rahul Akolkar 183 Steven Flores 187 Appendix B Roadmap Action Checklists 191 Step 1: Ideation 191 Step 2: Defining the Project 191 Step 3: Data Curation and Governance 192 Step 4: Prototyping 192 Step 5: Production 193 Thriving with an AI Lifecycle 193 Appendix C Pitfalls to Avoid 195 Step 1: Ideation 195 Step 2: Defining the Project 196 Step 3: Data Curation and Governance 199 Step 4: Prototyping 203 Step 5: Production 204 Thriving with an AI Lifecycle 206 Index 209
JEFFREY L. COVEYDUC is Vice President and Master Inventor at IBM. His diverse background consists of positions that encompass the creation of innovative, technologically advanced global AI solutions and client adoption. JASON L. ANDERSON is a Partner and CTO with the data consultancy, Comp Three, where he established a new AI line of business. He is also a former IBM Cognitive Architect and Master Inventor. He received both BS and MS degrees in Computer Science from California Polytechnic State University, SLO.
